As a real-world example, we investigate the bias in the representations in VGG16 [1]}, which is a pre-trained CNN model that is commonly used as feature extractor for downstream applications like image recognition DNN.
As test data, we use FairFace [2]}, a balanced data set of images of people from different age groups, races and binary genders.
Here, we choose the “race” variable as grouping to compute the topographic maps.
Moreover, as a random baseline to compare with, we add several groups of randomly picked examples.
We investigate the representations of a pre-trained VGG16 model, obtained from the TensorFlow Keras applicationshttps://github.com/keras-team/keras module, using the second maxpooling layer as an example.
In the appendix, we provide the VGG16 architecture (tab:models) and topographic maps of multiple layers of the network (fig:biaslayers).
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